Research articleClimate change and the economics of biomass energy feedstocks in semi-arid agricultural landscapes: A spatially explicit real options analysis
Introduction
De-carbonising global electricity generation is seen as key to stabilise atmospheric greenhouse gas levels (Edenhofer et al., 2014). Biomass production for use in electricity generation (hereafter biomass) is proposed as a renewable energy source that can contribute to the mitigation of climate change through direct CO2 sequestration and through the replacement of higher CO2 emitting fuels such as coal and oil (Bryan et al., 2008, Evans et al., 2010, Styles and Jones, 2007). The use of biomass (often in the form of agricultural residues, bagasse, forestry residues) is widespread globally, producing 280 TWh of electricity, equivalent to 1.5% of global electricity generation per annum (Eisentraut and Brown, 2012). But for biomass to play a significant role in future global energy supply, dedicated energy crops often grown on current agricultural land will be essential (Coleman and Stanturf, 2006, Evans et al., 2010).
Economically, biomass production has been found to be potentially competitive with conventional agricultural enterprises as the yields associated with production of woody perennials are often less sensitive to climatic variables and require fewer inputs (Bryan et al., 2010b, Heaton et al., 1999, Styles et al., 2008). In agricultural areas with variable climate and soil, the introduction of short rotation woody perennial production systems that use adapted woody species could provide a valuable diversification option. Moreover, it may offer the opportunity to buffer seasonal and annual variations in rainfall that cannot be reliably used by annual crops (Hobbs, 2009a). Internationally, where biomass supply chains are more developed, landholders have been slow in switching land use, particularly between agriculture and forested use despite potential profitability (Plantinga, 1996, Schatzki, 2003, Stavins and Jaffe, 1990). An explanation of this perceived investment inertia is that financial analysis of land use change has traditionally assumed the decision to switch land use can be modelled based on the Net Present Value (NPV) which compares current agricultural land uses with biomass alternatives (Yemshanov et al., 2015). However, several factors are commonly omitted from NPV analysis. Among them are sunk investment cost, investment irreversibility, significant uncertainty over future returns and flexibility in the timing of investment (Dixit and Pindyck, 1994, Trigeorgis, 1996). These omitted factors influence land holder decisions (Ihli et al., 2013) and lead to the erroneous NPV analysis conclusion that the land currently in agriculture would be more profitable in other forest based land uses (Frey et al., 2013, Parks, 1995, Stavins and Jaffe, 1990).
Real options analysis (ROA) has been proposed as a better model of investments decisions under conditions of uncertainty that are costly to reverse and where significant flexibility exists to delay investments (Dixit and Pindyck, 1994). ROA investment triggers, defined as the levels of revenue required to invest in a new land use, are often higher than NPV required returns if the investment involves inter-temporal opportunity costs (Musshoff, 2012). The effect of including ‘option values’ in investment decision analysis can be substantial (Regan et al., 2015, Schatzki, 2003). Unlike NPV analysis, the revenues required to trigger land use change must not only compensate the landholder for establishment cost and foregone returns from agriculture, but also for lost management flexibility and the revenue uncertainty from the new enterprise (Reeson et al., 2015).
Many of the key uncertainties influencing agricultural production such as rainfall, temperature and soil types vary spatially (Bryan et al., 2014). Heterogeneity of these factors has been widely included in NPV analysis in order to understand the spatial distribution of cost-effective land use change (Bateman, 2009, Bryan et al., 2010a, Crossman et al., 2011) which have found that landscape heterogeneity is likely to affect the location and timing of land use change. While qualitatively acknowledged, spatial variability has been largely overlooked in quantitative ROA of land use change. Limited exceptions demonstrating differing conversion threshold prices and conversion probabilities across space include Dumortier, 2013, Yemshanov et al., 2015 and Sanderson et al. (2016).
Another gap in the ROA of land use change is the effect of climate variability through time on yield. It has been shown to be the principal source of risk affecting long term economic viability of rain-fed agricultural systems in NPV assessments for semi-arid regions such as south east Australia (Kandulu et al., 2012). Despite NPV assessment showing that climate change is likely to provide landholders with additional production risks, surprisingly few studies have addressed the effect of climate change on agricultural land use change in a ROA framework. Hertzler, 2007, Hertzler et al., 2013 and Sanderson et al. (2016) are exceptions. They address these factors across an agricultural region with ROA employing spatial transects as an analogue for temporal changes due to climate change. There are limitations to this approach as temporal climate change effects are only roughly approximated by spatial transects. They exclude, for example, accounting for changing CO2 concentrations and their interactions with higher temperatures (Sanderson et al., 2016).
In this study we address both the gap in broad spatial coverage and the gap in accounting for climate change in ROA of land use changes. This study specifically modelled land use change from agriculture to biomass production in a spatially explicit framework across a broad region accounting for effects of climate change on yield variability. The analyses allow for the assessment of regional biomass industry viability with calculations and spatial mappings of areas where biomass land use is economically viable at several price points under alternative assumptions about climate change.
This article is organised as follows: The next section discusses the stochastic simulation-based real options model applied. This is followed by mapping the land use conversion to biomass with varying price and climate change assumptions. The final discussion focusses on how conclusions about regional biomass industry viability differ with ROA and traditional NPV analysis in the context of climate change futures.
Section snippets
Study area
Our study focused on the lower Murray region of southern Australia (Fig. 1). The dominant land use covering 50% of the region is rain-fed mixed farming, consisting of the dryland winter cropping of cereals (wheat, barley, oats), pulses (beans, lupins, peas), oilseeds (canola) and grazing of sheep (Bryan et al., 2011). The average farm size in the region is approximately 1000 ha (Kandulu et al., 2012). The region is typical of semi-arid rain dependant farming regions found globally. These
Regional primary productivity
Significant spatial heterogeneity of primary productivity was evident across the study area, determined primarily by rainfall and soil characteristics with greatest agricultural productivity in the high and medium rainfall areas. Modelling of both agricultural and biomass productivity suggest declines in both wheat and biomass production in response to warmer and drier climate futures in the low and medium rainfall zones (Fig. 3). But in the high rainfall zone, where winter crop production can
Discussion
The development of a large scale biomass industry in Australia has been suggested as a climate change resilient diversification option for agricultural landscapes. Currently, there are impediments to realising this potential. These include large reserves of accessible coal, the low cost of electricity generated in coal-fired power plants and uncertain greenhouse and renewable energy policy (Raison, 2006). Integrated tree processing is seen as a promising way forward for mallee based industries
Conclusions
The results presented here reaffirm that options values must be considered in land use change economics. However, the results demonstrate that the underlying variability in primary productivity has a substantial effect on the magnitude of uncertainty on the returns required to trigger land use change from wheat to biomass. Areas traditionally thought of as being quite similar on an average yield basis can display large differences in response to the inclusion of production and price risks due
Acknowledgements
This work was made possible by the Charles John Everard Scholarship awarded through the University of Adelaide and the support of CSIRO Sustainable Agriculture Flagship. The Authors wish to acknowledge Darran King and John Kandulu from CSIRO for proving spatial data sets and Matt Westlake and Waseem Kamleh from the University of Adelaide for assistance with high performance computing. The authors would like to recognise and thank the anonymous reviewers for their suggestions for improving this
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